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急性心肌梗死中与二硫化物诱导细胞死亡相关的基因及免疫微环境分析：一项生物信息学分析与验证

Disulfidptosis-related gene in acute myocardial infarction and immune microenvironment analysis: A bioinformatics analysis and validation.

作者信息

Huang Nan, Liu Chan, Liu Zheng, Lei Haibo

机构信息

Clinical Pharmacy, Xiangtan Center Hospital, Xiangtan, Hunan Province, PR China.

Clinical Pharmacy, Liuyang People's Hospital, Liuyang, Hunan Province, China.

出版信息

PLoS One. 2024 Dec 12;19(12):e0314935. doi: 10.1371/journal.pone.0314935. eCollection 2024.

DOI:10.1371/journal.pone.0314935
PMID:39666769
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11637291/
Abstract

Disulfidptosis is a newly discovered method of cell death. However, no studies have fully elucidated the role of disulfidptosis-related genes (DSRGs) in acute myocardial infarction (AMI). The potential role of DSRGs in AMI was analyzed through a comprehensive bioinformatics approach. Finally, hub genes were verified in vitro by qPCR. Sixteen DE-DSRGs were in the AMI. Thereafter, seven hub genes were determined by machine learning algorithms, which had potential diagnostic value in AMI. The risk model showed a robust diagnostic value (area under curve, AUC = 0.940). Prognostic analysis revealed the potential prognostic value of INF2 and CD2AP. Immune landscape analysis showed that hub genes were closely related to the immune microenvironment. By predictive analysis, we obtained four miRNAs, thirteen small molecule drugs, and five TFs closely related to hub genes. Experimental verification revealed that Slc3a2 and Inf2 were significantly up-regulated and Dstn was significantly down-regulated in the hypoxic model. Our study demonstrated that DSRGs are disorderedly expressed in AMI and identified seven hub genes through machine learning. In addition, a diagnostic model was constructed based on hub genes, providing a new perspective for the early diagnosis of AMI.

摘要

二硫化物诱导的细胞死亡是一种新发现的细胞死亡方式。然而，尚无研究充分阐明二硫化物诱导的细胞死亡相关基因（DSRGs）在急性心肌梗死（AMI）中的作用。通过综合生物信息学方法分析了DSRGs在AMI中的潜在作用。最后，通过qPCR在体外验证了枢纽基因。在AMI中有16个差异表达的DSRGs。此后，通过机器学习算法确定了7个枢纽基因，它们在AMI中具有潜在的诊断价值。风险模型显示出强大的诊断价值（曲线下面积，AUC = 0.940）。预后分析揭示了INF2和CD2AP的潜在预后价值。免疫景观分析表明，枢纽基因与免疫微环境密切相关。通过预测分析，我们获得了4个与枢纽基因密切相关的miRNA、13种小分子药物和5种转录因子。实验验证表明，在缺氧模型中，Slc3a2和Inf2显著上调，Dstn显著下调。我们的研究表明，DSRGs在AMI中表达紊乱，并通过机器学习鉴定了7个枢纽基因。此外，基于枢纽基因构建了诊断模型，为AMI的早期诊断提供了新的视角。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1ea/11637291/03f25a513cb8/pone.0314935.g010.jpg
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